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@ -76,20 +76,20 @@ For FP16 operators, if the input data type is FP32, the backend of MindSpore wil
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└─run_eval.sh # launch evaluating
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├─src
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├─backbone
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├─_init_.py # initalize
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├─_init_.py # initialize
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├─resnet.py # resnext50 backbone
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├─utils
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├─_init_.py # initalize
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├─_init_.py # initialize
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├─cunstom_op.py # network operation
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├─logging.py # print log
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├─optimizers_init_.py # get parameters
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├─sampler.py # distributed sampler
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├─var_init_.py # calculate gain value
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├─_init_.py # initalize
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├─_init_.py # initialize
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├─config.py # parameter configuration
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├─crossentropy.py # CrossEntropy loss function
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├─dataset.py # data preprocessing
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├─head.py # commom head
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├─head.py # common head
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├─image_classification.py # get resnet
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├─linear_warmup.py # linear warmup learning rate
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├─warmup_cosine_annealing.py # learning rate each step
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@ -140,7 +140,7 @@ You can start training by python script:
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python train.py --data_dir ~/imagenet/train/ --platform Ascend --is_distributed 0
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```
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or shell stript:
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or shell script:
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```script
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Ascend:
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@ -181,7 +181,7 @@ You can start training by python script:
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python eval.py --data_dir ~/imagenet/val/ --platform Ascend --pretrained resnext.ckpt
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```
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or shell stript:
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or shell script:
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```script
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# Evaluation
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